This session delves into learning analytics, emphasizing the pivotal role of direct measures in improving student outcomes in online education. Through interactive discussions, it explores how to distinguish between direct and indirect measures, integrate diverse data, and implement effective strategies for scalable interventions.

This presentation seeks to provide comprehensive insights into learning analytics, focusing on the crucial role of direct measures in enhancing student learning outcomes, particularly within the context of online education. The digital age has brought significant advancements in data collection and analysis technologies, making the understanding of how to leverage this data for actionable insights essential. This session explores the significance of distinguishing between direct and indirect measures in learning analytics and how to effectively integrate them into strategies that foster student growth and success. To start, the session will open with brief introductions, followed by an interactive poll designed to assess the audience's familiarity with learning analytics and their perspectives on direct and indirect measures. This initial engagement will shape the discussion, ensuring it addresses the specific needs and interests of the participants. The presentation will highlight lessons learned from a mid-sized university's efforts to gather and use direct measures, including high-stakes summative assessments and rubric data. The audience will learn about this institution’s approach to assembling and using comprehensive datasets to guide meaningful interventions and enhance student learning. Objective 1: Participants will explore the importance of differentiating between direct and indirect measures in learning analytics. While indirect measures such as student engagement proxies (e.g., clicks on content) are frequently used to predict outcomes like DFW rates, direct measures offer more accurate insights into student learning outcomes. By the end of the session, attendees will be able to articulate the importance of this distinction and understand its impact on intervention strategies. Objective 2: The session will also discuss the challenges and goals associated with achieving a fully integrated Learner Record Store (LRS). While centralizing all relevant data in one location for comprehensive analysis is the goal, architectural and implementation hurdles often make this difficult. Attendees will gain insights into integrating diverse data streams, the role of standards like Learning Technology Interoperability (LTI), and strategies for effectively addressing these challenges. Through interactive discussions and case studies, participants will understand how to navigate these complexities and work towards a more integrated approach to learning analytics. Objective 3: The session will offer practical strategies for implementing direct measures of learning, including gathering data from high-stakes assessments and rubrics. Attendees will learn about the complexities of aligning assessments, working with vendors to resolve data issues, and creating a fully integrated view of learning data. By understanding how to use artificial intelligence (AI) to scale design and alignment efforts, participants will discover how to gather, intervene, evaluate, monitor, and scale learning interventions effectively. To engage participants actively, the session will prioritize interactivity, including discussions, case studies, and hands-on activities that deepen understanding and facilitate knowledge sharing among attendees. By fostering a collaborative learning environment, participants will not only learn theoretical concepts but also gain practical insights and strategies that they can apply in their educational contexts. In summary, this session provides a comprehensive exploration of learning analytics, direct measures, and the challenges and strategies involved in scaling learning outcomes in online education. Attendees will leave with a deeper understanding of leveraging data effectively, distinguishing between direct and indirect measures, and implementing practical strategies to enhance learning outcomes in digital learning environments.